Multiparametric MRI-Based Radiomics Nomogram for Predicting Lymphovascular Space Invasion in Endometrial Carcinoma

Background Lymphovascular space invasion (LVSI) of endometrial carcinoma (EMC) is one of the important prognostic factors, which is not usually visible subjectively. Therefore, clinicians lack imaging-based evidence about LVSI for preoperative treatment decision-making. Purpose To develop a multiparametric MRI (mpMRI)-based radiomics nomogram for predicting LVSI in EMC and provide decision-making support to clinicians. Study Type Retrospective. Population In all, 144 patients with histologically confirmed EMC, 101 patients in a training cohort, and 43 patients in a test cohort. Field Strength/Sequence T2WI, contrast enhanced-T1WI, and diffusion-weighted imaging (DWI) at 3.0T MRI. Assessment Tumors were independently segmented images by two radiologists. Two pathologists reviewed the tissue specimens of the tumors to identify the existence of LVSI in consensus. Statistical Tests The intraclass correlation coefficient was used to test the reliability and least absolute shrinkage and selection operator (LASSO) regression for features selection and then developed a radiomics signature named Rad-score. A nomogram was developed in the training cohort. The diagnostic performance of the nomogram model was assessed by area under the curve (AUC) of the receiver operator characteristic (ROC) in the training and test cohort, respectively. Results LVSI was identified in 32 patients (22.2%). Older age and high grade were correlated with LVSI at univariate analysis. There were five radiomics features that were identified as independent risk factors for LVSI by LASSO regression. Based on age, grade, and Rad-score, the AUC values of the nomogram model to predict LVSI in the training and test cohort were 0.820 (95% confidence interval [CI]: 0.725, 0.916; sensitivity: 82.6%, specificity: 72.9%), 0.807 (95% CI: 0.673, 0.941; sensitivity: 77.8%, specificity: 78.6%), respectively. Data Conclusion The radiomic-based machine-learning model using a nomogram algorithm achieved high diagnostic performance for predicting LVSI of EMC preoperatively, which could enhance risk stratification and provide support for therapeutic decision-making. Level of Evidence 2. Technical Efficacy Stage 3.